Bob's face recognition packagehttp://gitlab.idiap.ch/bob/bob.bio.face has more than 30 different face recognition systems available (including traditional methods based on hand-crafted features, as well as many modern deep-learning algorithms and pre-trained networks) ready to be used.
Because of page limits, in this work we will make use of four different deep-learning-based face recognition systems available in Bob.
However, scores from all the available systems will be available.fn:package
The four systems are the following – in chronological order of publication:
The first system is the Facenet-Sandberghttps://github.com/davidsandberg/facenet trained using a pruned version of the MS-Celeb-1M dataset using the Inception-ResNet-v1 backbone [1]}.
The second network is the ArcFace model [2]} from InsightFace (ArcFace-100) that is based on ResNet-101 architecture and trained using the ArcFace loss.
The third network is taken from the FaceX-Zoo models [3]}, which contain several face recognition systems which are all integrated in Bob; in this work we have used the Zoo-AttentionNet backbone.
The forth method is based on ResNet-50 architecture trained using the ArcFace loss (Idiap-Resnet50) on a pruned version of the MSCeleb-1M dataset.
Finally, to show the improvement made over the last eight years, we also provide results of the top-performing method of our old evaluation, i.e., the Inter-Session Variability (ISV) modeling of Discrete Cosine Transform (DCT) features [4]}.
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